WLAN-based networks are being envisioned as the next-generation multi-service networks for enterprises, which utilize the same network to support a variety of services. Such networks provide a cost-effective solution to fulfill the communication needs of enterprises. Voice telephony still forms a popular communication mode in such an environment. Voice traffic, however, has stringent delay requirements and further, enterprise network users will have a greater expectation for high quality service than a typical residential user.
One source of degradation of voice quality in a mobile environment is due to handover latency. Since 802.11 coverage is much less than a cellular system, a typical enterprise voice over the Internet protocol (VoIP) user walking down a hallway might encounter multiple handovers within a voice call compounding the degradation of voice quality due to handovers.
There are three main measurement tasks that are performed by the subscriber unit in order to support subscriber-driven fast handover: measurement of serving access point (AP) signals, neighbor discovery, and neighbor tracking. An overview of the relationship between these processes within the subscriber unit 100 is illustrated in FIG. 1. Signals from the serving AP are measured by monitoring beacons from the serving AP. The serving AP is the AP in which the subscriber unit is currently associated with. In order to accomplish the other two tasks, i.e., neighbor discovery and neighbor tracking, the subscriber unit must take a “vacation” from its primary frequency.
In order to discover neighboring APs, the subscriber unit 100 periodically scans all the frequencies by broadcasting probe requests. Once the neighboring APs are discovered, the subscriber unit 100 tracks the signal strength of the neighboring APs that are perceived to be handover targets. The subscriber unit 100 performing neighbor tracking keeps track of the expected transmission time of the beacons from the neighboring APs, which aid signal strength measurements. The subscriber unit 100 subsequently uses the expected transmission times of the beacons to minimize the amount of time spent scanning the frequency.
For the current neighbor tracking algorithm, the responsibility of the measurement collection process is shared between the host processor 102 and the WLAN module 104. In particular, the host processor 102 schedules all the measurement collection processes, whereas the WLAN module 104 only executes the schedule by passively scanning the table of neighboring APs from a static schedule. At the end of the measurement collection, the WLAN module 104 provides the host processor 102 with measurement metrics, such as the received signal strength indicator (RSSI), etc.
Both the host processor 102 and the WLAN module 104 play a role in implementing neighbor tracking in the current algorithm. In the current algorithm, the neighbor tracking scheduler 106 resides in the host processor 102. The neighbor tracking scheduler 106 compiles a table comprising each neighboring AP to be passively scanned. Based upon the information it receives from the neighbor discovery scheduler 108, the neighbor tracking scheduler 106 determines the order in which the WLAN module 104 should take measurements and provides this information to the WLAN module 104 in a static schedule. The WLAN module 104 performs the neighbor tracking algorithm for the given neighboring APs in the exact order provided in the table compiled by the neighbor tracking scheduler 106 and reports the results back to the host processor 102.
A disadvantage to the current neighbor tracking algorithm is that the neighbor tracking scheduler 106 generates the static schedule dictating the order in which the WLAN module 104 is to listen for each neighboring AP. In other words, all the neighboring APs are passively scanned in a given order dictated a priori by the host processor 102, which does not change dynamically within a measurement window. Even if the neighbor discovery algorithm does a good job of identifying the expected transmission times of the beacons, there is no guarantee that the neighbor tracking algorithm will be able to effectively measure beacons transmitted from the neighboring APs. With contention-based systems, such as WLAN, the transmissions of the beacons can be significantly delayed due to loading at the neighboring AP. In addition, a variety of environmental factors, including multi-path, shadowing, fading, interference and collision may cause the subscriber unit to miss the detection of a beacon.
Current neighbor tracking algorithms compensate for the beacon uncertainty by either locking onto a neighboring channel and scanning the frequency for extended periods of time or by spacing AP measurements far enough apart in the time domain to avoid missing a beacon while scanning for a different one. As a result, the static schedule forces the WLAN module to enter a power save state after each measurement and wake up before the next measurement, thus requiring a full conventional warm up/warm down of the WLAN module before/after each passive scan.
A more intelligent neighbor tracking algorithm would take advantage of opportunities to make multiple passive neighbor scans each time the WLAN module is powered up. If properly executed, this intelligent algorithm would reduce the power drain contributed to warm down/up to less than the prior art's one warm down/up cycle per passive scan.